@article{Wolpaw2002767,
title = "Brain–computer interfaces for communication and control",
journal = "Clinical Neurophysiology",
volume = "113",
number = "6",
pages = "767 - 791",
year = "2002",
note = "",
issn = "1388-2457",
doi = "10.1016/S1388-2457(02)00057-3",
url = "http://www.sciencedirect.com/science/article/pii/S1388245702000573",
author = "Jonathan R Wolpaw and Niels Birbaumer and Dennis J McFarland and Gert Pfurtscheller and Theresa M Vaughan",
keywords = "Brain–computer interface",
keywords = "Electroencephalography",
keywords = "Augmentative communication",
keywords = "Rehabilitation",
keywords = "Neuroprosthesis",
keywords = "Brain–machine interface"
}


@ARTICLE{1214694,
author={Vaughan, T.M.},
journal={Neural Systems and Rehabilitation Engineering, IEEE Transactions on},
title={Guest editorial brain-computer interface technology: a review of the second international meeting},
year={2003},
month={june },
volume={11},
number={2},
pages={94 -109},
keywords={Algorithms;Artificial Limbs;Brain;Brain Mapping;Cerebral Cortex;Communication Aids for Disabled;Computer Systems;Disabled Persons;Electroencephalography;Evoked Potentials;Feedback;Humans;Models, Neurological;Neuromuscular Diseases;Prostheses and Implants;Robotics;Self-Help Devices;Signal Processing, Computer-Assisted;User-Computer Interface;},
doi={10.1109/TNSRE.2003.814799},
ISSN={1534-4320},}

@article{Delorme20021057,
title = "From single-trial EEG to brain area dynamics",
journal = "Neurocomputing",
volume = "44–46",
number = "0",
pages = "1057 - 1064",
year = "2002",
note = "Computational Neuroscience Trends in Research 2002",
issn = "0925-2312",
doi = "10.1016/S0925-2312(02)00415-0",
url = "http://www.sciencedirect.com/science/article/pii/S0925231202004150",
author = "A Delorme and S Makeig and M Fabre-Thorpe and T Sejnowski",
keywords = "EEG &amp; ERP",
keywords = "Visual categorization",
keywords = "ICA",
keywords = "Time-frequency analysis",
keywords = "Brain areas’ synchronizations"
}

@ARTICLE{4595650,
author={Bigdely-Shamlo, N. and Vankov, A. and Ramirez, R.R. and Makeig, S.},
journal={Neural Systems and Rehabilitation Engineering, IEEE Transactions on},
title={Brain Activity-Based Image Classification From Rapid Serial Visual Presentation},
year={2008},
month={oct. },
volume={16},
number={5},
pages={432 -441},
keywords={BCI;Bayes fusion;EEG;Fisher discriminant classifiers;brain-computer interface;electroencephalography;image classification;independent component analysis;rapid serial visual presentation;electroencephalography;image classification;independent component analysis;medical image processing;neurophysiology;Adult;Algorithms;Brain Mapping;Electroencephalography;Evoked Potentials, Visual;Female;Humans;Male;Pattern Recognition, Visual;Photic Stimulation;User-Computer Interface;Visual Cortex;},
doi={10.1109/TNSRE.2008.2003381},
ISSN={1534-4320},}

@ARTICLE{847807,
author={Wolpaw, J.R. and Birbaumer, N. and Heetderks, W.J. and McFarland, D.J. and Peckham, P.H. and Schalk, G. and Donchin, E. and Quatrano, L.A. and Robinson, C.J. and Vaughan, T.M.},
journal={Rehabilitation Engineering, IEEE Transactions on},
title={Brain-computer interface technology: a review of the first international meeting},
year={2000},
month={jun},
volume={8},
number={2},
pages={164 -173},
keywords={},
doi={10.1109/TRE.2000.847807},
ISSN={1063-6528},}

@article{Wolpaw21122004,
author = {Wolpaw, Jonathan R. and McFarland, Dennis J.},
title = {Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans},
volume = {101},
number = {51},
pages = {17849-17854},
year = {2004},
doi = {10.1073/pnas.0403504101},
abstract ={Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.},
URL = {http://www.pnas.org/content/101/51/17849.abstract},
eprint = {http://www.pnas.org/content/101/51/17849.full.pdf+html},
journal = {Proceedings of the National Academy of Sciences of the United States of America}
}

@article{Pfurtscheller19991842,
title = "Event-related EEG/MEG synchronization and desynchronization: basic principles",
journal = "Clinical Neurophysiology",
volume = "110",
number = "11",
pages = "1842 - 1857",
year = "1999",
note = "",
issn = "1388-2457",
doi = "10.1016/S1388-2457(99)00141-8",
url = "http://www.sciencedirect.com/science/article/pii/S1388245799001418",
author = "G. Pfurtscheller and F.H. Lopes da Silva",
keywords = "Event-related desynchronization (ERD)",
keywords = "Event-related synchronization (ERS)",
keywords = "Sensorimotor function",
keywords = "Voluntary movement",
keywords = "Brain oscillations"
}
@article{Roweis22122000,
author = {Roweis, Sam T. and Saul, Lawrence K.},
title = {Nonlinear Dimensionality Reduction by Locally Linear Embedding},
volume = {290},
number = {5500},
pages = {2323-2326},
year = {2000},
doi = {10.1126/science.290.5500.2323},
abstract ={Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.},
URL = {http://www.sciencemag.org/content/290/5500/2323.abstract},
eprint = {http://www.sciencemag.org/content/290/5500/2323.full.pdf},
journal = {Science}
}

@article{Belkin,
	location = {http://www.scientificcommons.org/43594511},
	title = {Laplacian eigenmaps and spectral techniques for embedding and clustering},
	author = {Mikhail Belkin and Partha Niyogi},
	year = {2002},
	abstract = {Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher dimensional space. The algorithm provides a computationally e cient approach tononlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering. Several applications are considered. In many areas of arti cial intelligence, information retrieval and data mining, one is often confronted with intrinsically low dimensional data lying in a very high dimensional space. For example, gray scalen n images of a xed object taken with amoving camera yield data points in Rn2.However, the intrinsic dimensionalityof the space of all images of the same object is the number of degrees of freedom of},
	publisher = {MIT Press},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.96.6841},
	institution = {CiteSeerX - Scientific Literature Digital Library and Search Engine [http://citeseerx.ist.psu.edu/oai2] (United States)},
}

@article{Donoho13052003,
author = {Donoho, David L. and Grimes, Carrie},
title = {Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data},
volume = {100},
number = {10},
pages = {5591-5596},
year = {2003},
doi = {10.1073/pnas.1031596100},
abstract ={We describe a method for recovering the underlying parametrization of scattered data (mi) lying on a manifold M embedded in high-dimensional Euclidean space. The method, Hessian-based locally linear embedding, derives from a conceptual framework of local isometry in which the manifold M, viewed as a Riemannian submanifold of the ambient Euclidean space ℝn, is locally isometric to an open, connected subset Θ of Euclidean space ℝd. Because Θ does not have to be convex, this framework is able to handle a significantly wider class of situations than the original ISOMAP algorithm. The theoretical framework revolves around a quadratic form ℋ(f) = ∫M ∥Hf(m)∥dm defined on functions f : M ↦ ℝ. Here Hf denotes the Hessian of f, and ℋ(f) averages the Frobenius norm of the Hessian over M. To define the Hessian, we use orthogonal coordinates on the tangent planes of M. The key observation is that, if M truly is locally isometric to an open, connected subset of ℝd, then ℋ(f) has a (d + 1)-dimensional null space consisting of the constant functions and a d-dimensional space of functions spanned by the original isometric coordinates. Hence, the isometric coordinates can be recovered up to a linear isometry. Our method may be viewed as a modification of locally linear embedding and our theoretical framework as a modification of the Laplacian eigenmaps framework, where we substitute a quadratic form based on the Hessian in place of one based on the Laplacian.},
URL = {http://www.pnas.org/content/100/10/5591.abstract},
eprint = {http://www.pnas.org/content/100/10/5591.full.pdf+html},
journal = {Proceedings of the National Academy of Sciences}
}

@ARTICLE{1704834,
author={Lafon, S. and Keller, Y. and Coifman, R.R.},
journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on},
title={Data Fusion and Multicue Data Matching by Diffusion Maps},
year={2006},
month={nov. },
volume={28},
number={11},
pages={1784 -1797},
keywords={Laplace-Beltrami approach;data assimilation;data fusion;diffusion maps;geometric harmonics;multicue data matching;data mining;graph theory;learning (artificial intelligence);sensor fusion;},
doi={10.1109/TPAMI.2006.223},
ISSN={0162-8828},}

@book{malmivuo1995bioelectromagnetism,
  title={Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields},
  author={Malmivuo, J. and Plonsey, R.},
  isbn={9780195058239},
  lccn={lc93042971},
  url={http://books.google.com.uy/books?id=H9CFM0TqWwsC},
  year={1995},
  publisher={Oxford University Press}
}

@article{Nadler2006113,
title = {Diffusion maps, spectral clustering and reaction coordinates of dynamical systems},
journal = "Applied and Computational Harmonic Analysis",
volume = "21",
number = "1",
pages = "113 - 127",
year = "2006",
note = "Special Issue: Diffusion Maps and Wavelets",
issn = "1063-5203",
doi = "10.1016/j.acha.2005.07.004",
url = "http://www.sciencedirect.com/science/article/pii/S1063520306000534",
author = "Boaz Nadler and Stéphane Lafon and Ronald R. Coifman and Ioannis G. Kevrekidis"
}

@article{Wang20042744,
title = {Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns},

journal = "Clinical Neurophysiology",

volume = "115",

number = "12",

pages = "2744 - 2753",

year = "2004",

note = "",

issn = "1388-2457",

doi = "10.1016/j.clinph.2004.06.022",

url = "http://www.sciencedirect.com/science/article/pii/S1388245704002445",

author = "Tao Wang and Jie Deng and Bin He",

keywords = "Brain–computer interface (BCI)",

keywords = "Electroencephalography (EEG)",

keywords = "Motor imagery",

keywords = "Event-related desynchronization (ERD)",

keywords = "Spatial correlation",

keywords = "Time–frequency weighting"

}
